Improvement on state estimation for discrete-time LTI systems with measurement loss

In this paper, we present a novel state estimation procedure for the LTI systems with loss of data at the output measurement channels. The proposed methodology aims at compensating such output measurement losses through an innovative design methodology which is based on the so-called linear prediction (LP) and Kalman filter theories. A compensated observation signal is first reconstructed using an LP subsystem and then supplied to a discrete-time Kalman filter in the closed-loop framework. We show that, under suitable assumptions, it is possible to reconstruct the lost data using an straightforward algorithm with the capability of associating an optimal filter order. A mass-spring-damper case study subject to measurement loss is provided to demonstrate some of the promising results of our proposed algorithm. Simulation results illustrate that the proposed estimation methodology is too far superior than those offered in the literature.